AI in Enhancing Zakat Collection and Distribution: A Comparative Study between Indonesia and India

Abstract

This comparative study investigates the transformative potential of artificial intelligence (AI) and other digital technological frameworks in reconceptualizing zakat collection and disbursement mechanisms across two distinctive Muslim-majority and Muslim-minority contexts: Indonesia and India, respectively. Indonesia's implementation trajectory indicates empirically validated outcomes via foundational digital infrastructure, demonstrating substantive enhancements in collection efficiency, institutional governance capacity, and operational accountability. The Indonesian experience, operating within the world's largest Muslim-majority nation, with established institutional frameworks such as BAZNAS, indicates measurable improvements in standardization and regulatory compliance. Conversely, in Indian context, despite advanced technological enterprise the zakah system is suffering from serious inefficiency due to rigidity of traditional zakat administration, with unique case of Muslims community which is one of the largest Muslim populations in the world but still a minority at approximately 14% of the total population of the country. Therein lies a compelling case for innovative solutions regarding the identification of donors, religious authentication, and decentralized distribution networks. Both technological interventions have considerable promise for increasing operational transparency, optimizing resource allocation, enhancing beneficiary identification precision, and mitigating intermediary inefficiencies. Synthesizing empirical evidence with theoretical constructs from Islamic economics and digital governance literature, this study illustrates how technological innovation can substantially reinforce Indian zakat's instrumental function within Islamic social finance ecosystems and poverty amelioration strategies, benefitting from Indonesian experience while addressing context-specific institutional, demographic, and regulatory contingencies.